J. Cuadrado-Gallego, Pablo Rodríguez-Soria, Borja Martín-Herrera
{"title":"Analogies and Differences between Machine Learning and Expert Based Software Project Effort Estimation","authors":"J. Cuadrado-Gallego, Pablo Rodríguez-Soria, Borja Martín-Herrera","doi":"10.1109/SNPD.2010.47","DOIUrl":null,"url":null,"abstract":"This paper presents a review and comparison of the software project cost estimation methods that have emerged with more impact in recent years; Expertise and Machine Learning methods. These methods and models have been selected according to an own criteria focusing onto Analogy estimation models and Case Based Reasoning approaches, assuming that they are widely utilized by researchers and with good accurate results. Finally we show a comparative analysis of the seven models proposed inside the Machine Learning methods with advantages and disadvantages between them.","PeriodicalId":266363,"journal":{"name":"2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SNPD.2010.47","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17
Abstract
This paper presents a review and comparison of the software project cost estimation methods that have emerged with more impact in recent years; Expertise and Machine Learning methods. These methods and models have been selected according to an own criteria focusing onto Analogy estimation models and Case Based Reasoning approaches, assuming that they are widely utilized by researchers and with good accurate results. Finally we show a comparative analysis of the seven models proposed inside the Machine Learning methods with advantages and disadvantages between them.